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Area of Science:

  • Computer Science
  • Data Compression
  • Algorithms

Background:

  • RePair is a grammar-based compression technique effective for creating compact grammars.
  • RePair's main limitation is its high memory usage, scaling poorly with input size.
  • Relative Lempel-Ziv (RLZ) parsing is a scalable, lightweight online method but may miss structural patterns.

Purpose of the Study:

  • To develop a scalable algorithm that constructs exact RePair grammars.
  • To leverage the strengths of both RePair and RLZ parsing for improved data compression.
  • To reduce memory usage and the number of bigram replacements in grammar-based compression.

Main Methods:

  • Introduced RLZ-RePair, an algorithm generating RePair grammars from RLZ parses.
  • Ensured systematic bigram replacements while preserving RLZ phrase integrity.
  • Systematically applied RePair iterations on RLZ parsed input.

Main Results:

  • RLZ-RePair achieves identical grammars to standard RePair with a well-chosen reference.
  • Demonstrated significant memory reduction: RePair used 18%–480% more memory.
  • RLZ-RePair requires fewer bigram replacements compared to standard RePair.

Conclusions:

  • RLZ-RePair offers a scalable solution for constructing exact RePair grammars.
  • This method results in a practical grammar-based compressor for large datasets.
  • RLZ-RePair combines the efficiency of RLZ parsing with the theoretical strengths of RePair.